Gradient-based approaches such as back-propagation are widely used for MLNN training, but have drawbacks such as being sensitive to the initial weights and a tendency to get stuck in local optima. Population-based metaheuristic (PBMH) algorithms such as particle swarm optimisation (PSO) [1]}, differential evolution [2]}, and human mental search (HMS) [3]}, [4]} are capable of overcoming these problems. PBMHs are problem-independent optimisation algorithms that find an optimal solution using a population of candidate solutions and some specific operators. Nowadays, these algorithms are extensively employed for MLNN training due to their simplicity, flexibility, and ability to escape local optima. PBMH-based training algorithms have been introduced using particle swarm optimisation (PSO) [5]}, [6]}, [7]}, artificial bee colony (ABC) [8]}, imperialist competitive algorithm (ICA) [9]}, [10]}, [11]}, firefly algorithm (FA) [12]}, grey wolf optimiser (GWO) [13]}, [14]}, ant lion optimiser [15]}, dragonfly algorithm (DA) [16]}, sine cosine algorithm [17]}, whale optimisation algorithm (WOA) [18]}, grasshopper optimisation algorithm [19]}, and salp swarm algorithm (SSA) [20]}, among others.
